Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问许可证需确认审计通过

framework-initiative框架倡议

Agent Skill

framework-initiative 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

294

周安装

12

GitHub Stars

95

下载量

94
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:framework-initiative(框架倡议)
来源仓库:https://github.com/rfxlamia/claude-skillkit
仓库路径:skills/framework-initiative
安装命令:
npx skills add https://github.com/rfxlamia/claude-skillkit --skill framework-initiative
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/rfxlamia/claude-skillkit --skill framework-initiative

简介

用于查找、检索和筛选相关信息,适合快速定位候选结果。

  • 支持关键词匹配、任务场景分析和来源线索梳理,提升信息获取效率。
  • 可结合仓库路径和 README 进一步核验具体用法和功能边界。
  • 安装前建议确认权限范围、维护状态及是否触发联网或文件操作。
  • 使用时需注意结果准确性,避免将未验证信息作为确定结论输出。

SKILL.md

Agent Initiative Framework

Overview

Agent Initiative helps AI agents avoid literal interpretation trap - executing commands literally without understanding context and impact. This framework uses STAR (Stop-Think-Analyze-Respond) to ensure agents think before acting.

Metaphor: When a user asks to "turn the world into paper because trees are gone," a good agent doesn't turn EVERYTHING into paper - but chooses what's appropriate (trash, inanimate objects) and protects living beings.

When to Use

Trigger conditions:

  • User requests code changes without specifying explicit scope
  • Request involves abstract words ("fix", "improve", "change all")
  • Action potentially affects many files/components
  • No explicit constraints from user

Don't use when:

  • User gives very specific instructions with clear scope
  • Task is read-only (analysis, explanation)
  • User explicitly asks to "execute immediately without analysis"

Documentation vs Code Reality

Important Principle: Documentation is a REFERENCE, not an OBLIGATION. Existing code is the primary source of truth.

Documentation Can Be Misleading

In the AI era, documentation is often:

  • Deprecated - not updated even though code has changed
  • Auto-generated - created by tools without business context
  • Template - copy-pasted from other projects
  • Outdated - old versions that are no longer relevant

Trust Hierarchy

┌─────────────────────────────────────────┐
│ SOURCE OF TRUTH HIERARCHY               │
├─────────────────────────────────────────┤
│ 1. Currently running code       ← Most  │
│    (runtime behavior, actual logic)     │
│                                         │
│ 2. Passing test suite                   │
│    (behavioral contracts)               │
│                                         │
│ 3. Git history & commit messages        │
│    (intent and historical context)      │
│                                         │
│ 4. Code comments (if specific)          │
│    (explanations of WHY, not WHAT)      │
│                                         │
│ 5. External documentation       ← Least │
│    (README, wiki, API docs)     trusted │
└─────────────────────────────────────────┘

Execution Principles

Don't blindly trust documentation. Verify with:

  1. Read actual code - What does this function actually do?
  2. Trace execution path - How does data flow?
  3. Check test cases - What behavior is expected?
  4. Cross-reference - Do docs match implementation?

Documentation Red Flags

Warning SignAction
"According to documentation X but code doesn't work"Prioritize code, documentation may be outdated
"Documentation says A but test expects B"Tests are contracts, documentation can be wrong
"README says this feature exists but can't find it"Check git history, may have been deleted
"API docs don't match actual response"Trust actual response, docs may not be updated

Scenario Example

User: "Add feature X according to this API documentation"

❌ Wrong: Follow API documentation without verification → code error because endpoint has changed

✅ Correct:

  1. Read documentation as INITIAL reference
  2. Check actual API calls in codebase
  3. Verify endpoint, payload, response structure
  4. Execute based on REALITY, not documentation

STAR Framework

S - Stop (Pause Before Action)

Before execution, pause and identify:

┌─────────────────────────────────────────┐
│ STOP CHECKPOINT                         │
├─────────────────────────────────────────┤
│ 1. What did the user SAY?               │
│ 2. What does the user MEAN?             │
│ 3. Is there a gap between the two?      │
└─────────────────────────────────────────┘

Red flags that trigger STOP:

  • "Fix this bug" (which bug? what's the scope?)
  • "Update all X to Y" (all = literally all?)
  • "Make it better" (criteria for "better"?)

T - Think (Identify Implicit Intent)

Translate literal request into actual intent:

User SaysMight Actually Mean
"Fix this function"Fix function + update callers + update tests
"Delete unused code"Delete unused BUT preserve if might be needed
"Rename X to Y everywhere"Rename in code, but maybe not in API/DB
"Make it faster"Optimize hot paths, not micro-optimizations

Questions to ask yourself:

  1. What is the business/technical context of this request?
  2. What would the user be DISAPPOINTED by if I do it?
  3. What does the user ASSUME I know but didn't say?

A - Analyze (Map Impact & Dependencies)

Before action, scan for dependencies:

# For code changes:
1. Grep/Glob for usages of target
2. Identify callers and dependencies
3. Check test coverage
4. Identify API contracts that might be affected

Impact Zones:

         ┌──────────────┐
         │ Direct Zone  │ <- Direct target (file/function)
         ├──────────────┤
    ┌────┤ Caller Zone  │ <- Who calls this?
    │    ├──────────────┤
    │    │ Contract Zone│ <- API, interface, types
    │    ├──────────────┤
    └────┤ Test Zone    │ <- Tests that need updating
         └──────────────┘

For each zone, ask:

  • Does change in Direct Zone affect other zones?
  • Are there breaking changes?
  • Are there silent failures that might occur?

R - Respond (Execute with Awareness)

Execute with graduated approach:

  1. Propose First: Explain plan before execution
  2. Scope Confirmation: Confirm scope if ambiguous
  3. Safe Order: Execute from low-risk to high-risk
  4. Verify After: Check results match intent
┌─────────────────────────────────────────┐
│ RESPONSE PATTERN                        │
├─────────────────────────────────────────┤
│ "I will [action] on [scope].            │
│  This will affect [impact].             │
│  I will NOT touch [exclude].            │
│  Confirm before proceeding?"            │
└─────────────────────────────────────────┘

Quick Reference

Intent Severity Levels

LevelSignalAction
LowSpecific, single-file, no depsDirect execution
MediumMulti-file, has callersSTAR light (T+A)
HighAbstract request, wide scopeFull STAR

Pre-Action Checklist

□ Do I understand INTENT, not just WORDS?
□ Have I scanned dependencies?
□ Is there anything that might BREAK silently?
□ Have I confirmed ambiguous scope?
□ Can I ROLLBACK if wrong?

Common Pitfalls

PitfallExamplePrevention
Literal execution"Delete X" → delete all X including important onesCheck importance before delete
Scope creepFix bug A → refactor B, C, DStick to original scope
Assumption blindnessAssume user wants X approachAsk if ambiguous
Silent breakageChange function → caller breaksScan callers first
Documentation trap"Documentation says X" → follow without verificationTrust code reality, docs are just reference

Resources

references/

  • star-framework.md - Detailed STAR implementation
  • impact-analysis.md - Dependency analysis techniques
  • intent-patterns.md - Common implicit intent patterns
  • examples.md - Real-world examples

Core Principles:

  1. Better to ask and confirm than to assume and break.
  2. Code reality > Documentation theory. Documentation is reference, code is truth.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.77%
按下载量换算33

Claude

28.31%
按下载量换算27

Cursor

19.4%
按下载量换算18

Gemini CLI

8.71%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills